Hypertension affects 50 million Americans and is the single greatest risk factor contributing to diseases of the brain, heart, and kidneys. There is a strong evidence that hypertension has a genetic basis. In this application, we propose to develop novel approaches to better understand the genetic mechanisms contributing to measures of blood pressure (BP) level, diagnostic category (hypertension versus normotension) and correlated traits. To achieve this goal, in the aim 1 we propose to localize genes influencing measures of blood pressure levels, diagnostic category and their correlates. To do so we will apply genome-wide multivariate linkage analyses based on the variance components approach and utilizing clusters of traits correlated with measures of blood pressure and/or diagnostics category. In the aim 2, we propose to develop exploratory diagnostic tools for linkage analysis of complex traits to further enhance our ability to localize genes influencing measures of blood pressure, diagnostic category and their correlates. To do so, we will extend the diagnostic tools used in regression analysis to the variance components approach used for linkage analysis of quantitative traits. In our case, for example, it can be used to identify outlier families since previous studies have shown that families with outlier values yield false-positive results. We will also extend tree-structure models to pedigree data. Tree-based modeling is an exploratory technique for uncovering structure in the data. The use of tree-structure models is advantageous because no assumptions are necessary to explore the data structure or to derive parsimonious model. These models are accurate classifiers (binary outcome) and predictors (quantitative outcomes). Thus, they will help us to better understand the underlying structure of the familial data. All these tools will be incorporated in the S-Plus software as a function. S-Plus was selected due to its capability and flexibility for analyzing large data sets. We will analyze family data from the Rochester Family Heart Study, collected in previously NIH funded grants (R01 HL30428 "Sodium Transport: Genetics and Hypertension", and R01 HL51021 "Molecular Epidemiology of Essential Hypertension"). No new data will be collected for this grant proposal. The application clearly fits the goal of this NHLBI grant program since it will explore new approaches using an existing data set collected through NHLBI support.